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A trajectory-level robot skill learning method based on fuzzy systems

science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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LucidGrasp: Robotic Framework for Autonomous Manipulation of laboratory Equipment with Different Degrees of Transparency via 6D Pose Estimation

LucidGrasp: Robotic Framework for Autonomous Manipulation of...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Makarova, Maria Trinitatova, Daria Liu, Qian Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Moscow121205 Russia Dalian University of Technology Department of Computer Science and Technology China

ISBN: (纸本)9781665481090

Many modern robotic systems operate autonomously, however they often lack the ability to accurately analyze the environment and adapt to changing external conditions, while teleoperation systems often require special operator skills. In the field of laboratory automation, the number of automated processes is growing, however such systems are usually developed to perform specific tasks. In addition, many of the objects used in this field are transparent, making it difficult to analyze them using visual channels. The contributions of this work include the development of a robotic framework with autonomous mode for manipulating liquid-filled objects with different degrees of transparency in complex pose combinations. The conducted experiments demonstrated the robustness of the designed visual perception system to accurately estimate object poses for autonomous manipulation, and confirmed the performance of the algorithms in dexterous operations such as liquid dispensing. The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability. © 2024 IEEE.

关键词： Remote control

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Bifunctional nanoprobe for simultaneous detection of intracellular reactive oxygen species and temperature in single cells

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Microsystems & Nanoengineering 2024年第6期10卷 161-172页

作者： Yanmei Ma Weikang Hu Jian Hu Muyang Ruan Jie Hu Ming Yang Yi Zhang Hanhan Xie Chengzhi Hu Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518000China Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhen 518000China

Living cells can rapidly adjust their metabolic activities in response to external stimuli,leading to fluctuations in intracellular temperature and reactive oxygen species(ROS)*** these parameters is essential for understanding cellular metabolism,particularly during dynamic biological *** this study,we present a bifunctional nanoprobe capable of simultaneous measurement of ROS levels and temperature within single *** nanoprobe features two individually addressable nanoelectrodes,with platinum(Pt)and nickel(Ni)coatings on both *** the tip,these two metal layers form a nano-thermocouple,enabling precise intracellular temperature measurements,while the Pt layer facilitates selective ROS *** dual functionality allows for real-time monitoring of cellular responses during synergistic chemo-photothermal therapy of cancer cells and zebrafish embryos subjected to mitochondrial toxic *** results demonstrate that the nanoprobe effectively measures increases in temperature and ROS levels in HeLa cells undergoing chemo-photothermal therapy,as well as in chemically stimulated zebrafish *** providing detailed analysis of submicrometer-scale temperature and ROS variations within living cells,this nanoprobe offers valuable insights into cellular processes and holds promise for early disease detection and drug development.

关键词： metabolism simultaneous holds

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年第9期9卷 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive Control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications

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Nano-Micro Letters 2024年第3期16卷 1-35页

作者： Sourav Dutta Seungmin Noh Roger Sanchis Gual Xiangzhong Chen Salvador Pané Bradley J.Nelson Hongsoo Choi Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu 42988Republic of Korea DGIST‑ETH Microrobotics Research Center DGISTDaegu 42988Republic of Korea Multi‑Scale Robotics Lab Institute of Robotics and Intelligent SystemsETH Zurich8092 ZurichSwitzerland Institute of Optoelectronics State Key Laboratory of Photovoltaic Science and TechnologyShanghai Frontiers Science Research Base of Intelligent Optoelectronics and PerceptionFudan UniversityShanghai 200433People’s Republic of China

Synthetic micromotor has gained substantial attention in biomedicine and environmental ***-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen *** novel feature has projected degradable metallic micromotors for active in vivo drug delivery *** addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and *** propulsion efficiency and life span govern the overall performance of a degradable metallic *** we review the design and recent advancements of metallic degradable ***,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical ***,we discuss micromotors’efficacy in detecting and destroying environmental ***,we address the limitations and future research directions of degradable metallic micromotors.

关键词： Magnesium Zinc Iron Biodegradable microrobot Biomedical Environmental

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Heat Transfer Area Optimization for Heat Exchanger System

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computer Modeling in Engineering & sciences 2025年第4期143卷 335-349页

作者： Yu-Cheng Liao Fu-I Chou Po-Yuan Yang Jyh-Horng Chou Department of Electrical Engineering National Kaohsiung University of Science and TechnologyKaohsiung807Taiwan Department of Intelligent Robotics National Pingtung UniversityPingtung900Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia UniversityTaichung407Taiwan

This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi ***-GSO algorithm is used to optimize the heat transfer area of the heat exchanger *** R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global *** conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout ***,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system ***,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the *** optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are ***,fixing the systemoperating temperature parameters at a constant value is *** paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger *** results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.

关键词： Heat exchanger system allowable tolerance-based group search optimization Taguchi method tolerance design

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Reaching and Grasping with NAO Robot 16th

Reaching and Grasping with NAO Robot

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Borghese, N. Alberto Chitti, Eleonora Laboratory of Applied Intelligent Systems Department of Computer Science University of Milan Milan Italy

ISBN: (纸本)9789819635184

In this paper we propose a robust approach that combines classical computer vision with a smart setup that enables a NAO humanoid robot to play with children. Blocks are adopted as playing objects as they are widely used and are a highly versatile support. This approach addresses the challenges of object reaching and grasping, by allowing the robot to approach the table over which activity is carried out, orient appropriately, plan the reaching movement that is executed partly through direct and partly through inverse kinematics, grasp the block, moving it over the table and releasing it into its target position. By integrating visual information provided by the robot cameras into a closed-loop control system, no additional sensors are required. This approach enables not only children to play with a Humanoid robot in a richer way, but also to be used to treat cognitive / motor deficits inside what is termed Robot Assisted Therapy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anthropomorphic robots

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ARnopoly: exploring strengths and weaknesses of AR experience enhancing board games 3

ARnopoly: exploring strengths and weaknesses of AR experienc...

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3rd Workshop on Artificial Intelligence for Human-Machine Interaction, AIxHMI 2024

作者： Chitti, Eleonora Barbagallo, Andrea Delia, Andrea Borghese, N. Alberto Applied Intelligent Systems Laboratory Department of Computer Science University of Milan Italy

This project aims to explore and apply the best practices of Augmented Reality (AR) design to the board play. We adapted the Monopoly game, developing an AR version that integrates the traditional game with new elements to take advantage of the virtual world. Our goal is to explore which AR game rules and mechanics can be incorporated into board game design to create a more engaging and effective experience for players. With ARnopoly, we specifically examined the strengths of augmented reality and the weaknesses that need to be addressed to seamlessly integrate the physical board play with the digital experience. ARnopoly was tested involving 7 participants, and from the analysis of their feedback it emerged that the integration of the board play with AR increases the immersion in the game and the overall positive feeling. However, in the long run, since the monopoly-like game is very long-lasting, this positivity is dampened by the constraint of holding the device in hand which causes fatigue in the user. © 2024 for this paper by its authors.

关键词： Game design

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EDSD: efficient driving scenes detection based on Swin Transformer

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Multimedia Tools and Applications 2024年第39期83卷 87179-87198页

作者： Chen, Wei Zheng, Ruihan Jiang, Jiade Tian, Zijian Zhang, Fan Liu, Yi Beijing100083 China Key Laboratory of Intelligent Mining and Robotics Ministry of Emergency Management Beijing100083 China School of Computer Science & amp Technology China University of Mining and Technology Xuzhou221116 China

In the field of autonomous driving, the detection of targets such as vehicles, bicycles, and pedestrians in complex road conditions is of great importance. Through extensive experimentation, we have found that various vehicle targets generally occupy large sizes in the image but are easily occluded, while small targets such as pedestrians usually appear densely. The detection of targets of different sizes is an important challenge for the performance of current detectors. To address this issue, we proposed a novel hierarchical feature pyramid network structure. This structure comprises a series of CNN-Transformer variant layers, each of which is a superposition of CST neural network modules and Swin Transformer modules. In addition, considering that the huge computation of the global self-attention mechanism is difficult to be applied in the field of autonomous driving, we adopted the shifted window method in SwinFM, which effectively accelerates the inference process by replacing the traditional method by using the self-attention mechanism within the window. This study uses the Swin Transformer as a baseline. Compared to the baseline, our EDSD model improves the average accuracy by 1.8% and 3.1% on the BDD100K dataset and the KITTI dataset, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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